def __init__(self,
MAX_HORIZONTAL_GAP=30,
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6):
"""
pass
"""
self.text_proposal_connector = TextProposalConnector(
MAX_HORIZONTAL_GAP, MIN_V_OVERLAPS, MIN_SIZE_SIM)
class TextDetector:
"""
Detect text from an image
"""
def __init__(self,
MAX_HORIZONTAL_GAP=30,
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6):
"""
pass
"""
self.text_proposal_connector = TextProposalConnector(
MAX_HORIZONTAL_GAP, MIN_V_OVERLAPS, MIN_SIZE_SIM)
def detect(self,
text_proposals,
scores,
size,
TEXT_PROPOSALS_MIN_SCORE=0.7,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.3,
MIN_RATIO=1.0,
LINE_MIN_SCORE=0.8,
TEXT_PROPOSALS_WIDTH=5,
MIN_NUM_PROPOSALS=1):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
@@param:TEXT_PROPOSALS_MIN_SCORE:TEXT_PROPOSALS_MIN_SCORE=0.7##过滤字符box阀值
@@param:TEXT_PROPOSALS_NMS_THRESH:TEXT_PROPOSALS_NMS_THRESH=0.3##nms过滤重复字符box
@@param:TEXT_LINE_NMS_THRESH:TEXT_LINE_NMS_THRESH=0.3##nms过滤行文本重复过滤阀值
@@param:MIN_RATIO:MIN_RATIO=1.0#0.01 ##widths/heights宽度与高度比例
@@param:LINE_MIN_SCORE:##行文本置信度
@@param:TEXT_PROPOSALS_WIDTH##每个字符的默认最小宽度
@@param:MIN_NUM_PROPOSALS,MIN_NUM_PROPOSALS=1##最小字符数
"""
#text_proposals, scores=self.text_proposal_detector.detect(im, cfg.MEAN)
keep_inds = np.where(scores > TEXT_PROPOSALS_MIN_SCORE)[0] ###
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices]
# nms for text proposals
if len(text_proposals) > 0:
keep_inds = nms(np.hstack((text_proposals, scores)),
TEXT_PROPOSALS_NMS_THRESH) ##nms 过滤重复的box
text_proposals, scores = text_proposals[keep_inds], scores[
keep_inds]
scores = normalize(scores)
text_lines = self.text_proposal_connector.get_text_lines(
text_proposals, scores, size) ##合并文本行
return text_lines
else:
return []
class TextDetector:
"""
Detect text from an image
"""
def __init__(self,
MAX_HORIZONTAL_GAP=100,
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6):
"""
pass
"""
self.text_proposal_connector = TextProposalConnector(
MAX_HORIZONTAL_GAP, MIN_V_OVERLAPS, MIN_SIZE_SIM)
def detect(self,
text_proposals,
scores,
size,
TEXT_PROPOSALS_MIN_SCORE=0.1,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.99,
LINE_MIN_SCORE=0.1):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
@@param:TEXT_PROPOSALS_MIN_SCORE:TEXT_PROPOSALS_MIN_SCORE=0.7##过滤字符box阀值
@@param:TEXT_PROPOSALS_NMS_THRESH:TEXT_PROPOSALS_NMS_THRESH=0.3##nms过滤重复字符box
@@param:TEXT_LINE_NMS_THRESH:TEXT_LINE_NMS_THRESH=0.3##nms过滤行文本重复过滤阀值
@@param:LINE_MIN_SCORE:##行文本置信度
"""
#text_proposals, scores=self.text_proposal_detector.detect(im, cfg.MEAN)
keep_inds = np.where(scores > TEXT_PROPOSALS_MIN_SCORE)[0] ###
text_proposals, scores = text_proposals[keep_inds], scores[
keep_inds] #1347,4
sorted_indices = np.argsort(scores.ravel())[::-1] # 获取分数顺序排列的索引
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices] #1347,4
# nms for text proposals
if len(text_proposals) > 0:
text_proposals, scores = nms(text_proposals, scores,
TEXT_PROPOSALS_MIN_SCORE,
TEXT_PROPOSALS_NMS_THRESH)
scores = normalize(scores)
text_lines, scores = self.text_proposal_connector.get_text_lines(
text_proposals, scores, size) ##合并文本行
text_lines = get_boxes(text_lines)
text_lines, scores = rotate_nms(text_lines, scores, LINE_MIN_SCORE,
TEXT_LINE_NMS_THRESH)
return text_lines, scores
else:
return []
class TextDetector:
"""
Detect text from an image
"""
def __init__(self,
MAX_HORIZONTAL_GAP=30,
MIN_V_OVERLAPS=0.6,
MIN_SIZE_SIM=0.6):
"""
pass
"""
self.text_proposal_connector = TextProposalConnector(
MAX_HORIZONTAL_GAP, MIN_V_OVERLAPS, MIN_SIZE_SIM)
def detect_region(
self,
text_proposals,
scores,
size,
TEXT_PROPOSALS_MIN_SCORE=0.7,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.3,
):
"""
Detecting texts from an image
:return: the bounding boxes of the detected texts
@@param:TEXT_PROPOSALS_MIN_SCORE:TEXT_PROPOSALS_MIN_SCORE=0.7##过滤字符box阀值
@@param:TEXT_PROPOSALS_NMS_THRESH:TEXT_PROPOSALS_NMS_THRESH=0.3##nms过滤重复字符box
@@param:TEXT_LINE_NMS_THRESH:TEXT_LINE_NMS_THRESH=0.3##nms过滤行文本重复过滤阀值
@@param:MIN_RATIO:MIN_RATIO=1.0#0.01 ##widths/heights宽度与高度比例
@@param:LINE_MIN_SCORE:##行文本置信度
@@param:TEXT_PROPOSALS_WIDTH##每个字符的默认最小宽度
@@param:MIN_NUM_PROPOSALS,MIN_NUM_PROPOSALS=1##最小字符数
"""
#text_proposals, scores=self.text_proposal_detector.detect(im, cfg.MEAN)
keep_inds = np.where(scores > TEXT_PROPOSALS_MIN_SCORE)[0] ###
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices]
# nms for text proposals
keep_inds = nms(np.hstack((text_proposals, scores)),
TEXT_PROPOSALS_NMS_THRESH,
GPU_ID=self.GPU_ID) ##nms 过滤重复的box
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
groups_boxes, groups_scores = self.text_proposal_connector.get_text_region(
text_proposals, scores, size)
return groups_boxes, groups_scores
def detect(self,
text_proposals,
scores,
size,
TEXT_PROPOSALS_MIN_SCORE=0.7,
TEXT_PROPOSALS_NMS_THRESH=0.3,
TEXT_LINE_NMS_THRESH=0.3,
bili=1.2):
"""#本函数抽取全部的文字!
Detecting texts from an image
:return: the bounding boxes of the detected texts
@@param:TEXT_PROPOSALS_MIN_SCORE:TEXT_PROPOSALS_MIN_SCORE=0.7##过滤字符box阀值
@@param:TEXT_PROPOSALS_NMS_THRESH:TEXT_PROPOSALS_NMS_THRESH=0.3##nms过滤重复字符box
@@param:TEXT_LINE_NMS_THRESH:TEXT_LINE_NMS_THRESH=0.3##nms过滤行文本重复过滤阀值
@@param:MIN_RATIO:MIN_RATIO=1.0#0.01 ##widths/heights宽度与高度比例
@@param:LINE_MIN_SCORE:##行文本置信度
@@param:TEXT_PROPOSALS_WIDTH##每个字符的默认最小宽度
@@param:MIN_NUM_PROPOSALS,MIN_NUM_PROPOSALS=1##最小字符数
"""
#text_proposals, scores=self.text_proposal_detector.detect(im, cfg.MEAN)
text_proposalsOld = text_proposals
scoresOld = scores
keep_inds = np.where(
scores > TEXT_PROPOSALS_MIN_SCORE)[0] ###阈值赛选,去掉没用的box
#这个TEXT_PROPOSALS_MIN_SCORE要变小!!!!!!!!!!!!!!!!!!
text_proposals, scores = text_proposals[keep_inds], scores[keep_inds]
sorted_indices = np.argsort(scores.ravel())[::-1]
text_proposals, scores = text_proposals[sorted_indices], scores[
sorted_indices]
# nms for text proposals
'''
2019-09-15,18点13
今天经过大量的测试.发现这个拼接算法还是有缺陷,因为文字边缘的子图片的score一定会相对小的.所以
真正的score判定应该是一个局部的正态分布.越中心的需要得分越高.也就是说有一个平滑过程.
需要对这个算法做继续的优化.可以保留前面的算法,再后面进行补充添加.也就是说,让权重设一个小一点的
后面进行召回算法.把不符合的去掉
'''
if len(text_proposals) > 0: #nms里面需要传2个参数第一个是box+score 第二个是阈值.
#打算把下面一行的nms改成soft-nms
keep_inds = nms(np.hstack((text_proposals, scores)),
TEXT_PROPOSALS_NMS_THRESH) ##nms 过滤重复的box
keep_indsForSingle = keep_inds
#下面一行的text_proposals 表示的是框单个文字的框, scores也是单个文字的得分
text_proposals, scores = text_proposals[keep_inds], scores[
keep_inds]
#当前的scores就是那些小块的分数.
scoresBeforeNormalize = scores
scores = normalize(scores) # 归一化 #
#下面一行是核心!!!!!!!!!!!,进行文字拼接操作!!!!!!!!!!!!!!!!!!!!!!!!!!! size:图片大小参数
text_lines, tp_groups = self.text_proposal_connector.get_text_lines(
text_proposals, scores, size, scoresBeforeNormalize,
TEXT_PROPOSALS_MIN_SCORE, bili) ##合并文本行
#text_lines是 一行表示成为一个框.
keep_inds = nms(text_lines, TEXT_LINE_NMS_THRESH) ##nms
text_lines = text_lines[keep_inds]
tp_groups = tp_groups[keep_inds]
scoreFinal = text_lines[:, 4]
return text_lines, scoreFinal, keep_indsForSingle, tp_groups, text_proposals, scoresBeforeNormalize
else:
return [], np.array([]), np.array([]), [], np.array([]), np.array(
[])